Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations97306
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory5.8 MiB
Average record size in memory62.0 B

Variable types

Numeric9
Boolean4
Categorical1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
alcohol_use is highly overall correlated with cigarette_useHigh correlation
cigarette_use is highly overall correlated with alcohol_useHigh correlation
father_age is highly overall correlated with mother_ageHigh correlation
mother_age is highly overall correlated with father_ageHigh correlation
plurality is highly imbalanced (89.8%)Imbalance
cigarette_use is highly imbalanced (77.6%)Imbalance
alcohol_use is highly imbalanced (77.2%)Imbalance
baby_alive is highly imbalanced (61.2%)Imbalance
ever_born is highly skewed (γ1 = 71.8577652)Skewed

Reproduction

Analysis started2024-07-30 18:05:09.990214
Analysis finished2024-07-30 18:05:38.922386
Duration28.93 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.374
Minimum1969
Maximum2008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.2 KiB
2024-07-30T18:05:39.147993image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1969
5-th percentile1986
Q12005
median2006
Q32007
95-th percentile2008
Maximum2008
Range39
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.0675082
Coefficient of variation (CV)0.0035260427
Kurtosis10.083389
Mean2004.374
Median Absolute Deviation (MAD)1
Skewness-3.2390583
Sum1.9503761 × 108
Variance49.949672
MonotonicityNot monotonic
2024-07-30T18:05:39.623606image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2007 23362
24.0%
2008 23176
23.8%
2006 20064
20.6%
2005 19283
19.8%
1997 397
 
0.4%
2004 386
 
0.4%
1982 382
 
0.4%
1996 372
 
0.4%
1998 372
 
0.4%
2000 360
 
0.4%
Other values (30) 9152
 
9.4%
ValueCountFrequency (%)
1969 309
0.3%
1970 336
0.3%
1971 214
0.2%
1972 199
0.2%
1973 174
0.2%
1974 194
0.2%
1975 195
0.2%
1976 250
0.3%
1977 316
0.3%
1978 299
0.3%
ValueCountFrequency (%)
2008 23176
23.8%
2007 23362
24.0%
2006 20064
20.6%
2005 19283
19.8%
2004 386
 
0.4%
2003 324
 
0.3%
2002 338
 
0.3%
2001 341
 
0.4%
2000 360
 
0.4%
1999 358
 
0.4%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5483526
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.2 KiB
2024-07-30T18:05:40.020757image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4114625
Coefficient of variation (CV)0.52096499
Kurtosis-1.18096
Mean6.5483526
Median Absolute Deviation (MAD)3
Skewness-0.02487014
Sum637194
Variance11.638076
MonotonicityNot monotonic
2024-07-30T18:05:40.390823image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 8735
9.0%
7 8649
8.9%
9 8329
8.6%
6 8167
8.4%
5 8163
8.4%
3 8133
8.4%
10 8088
8.3%
12 8027
8.2%
1 7867
8.1%
4 7847
8.1%
Other values (2) 15301
15.7%
ValueCountFrequency (%)
1 7867
8.1%
2 7464
7.7%
3 8133
8.4%
4 7847
8.1%
5 8163
8.4%
6 8167
8.4%
7 8649
8.9%
8 8735
9.0%
9 8329
8.6%
10 8088
8.3%
ValueCountFrequency (%)
12 8027
8.2%
11 7837
8.1%
10 8088
8.3%
9 8329
8.6%
8 8735
9.0%
7 8649
8.9%
6 8167
8.4%
5 8163
8.4%
4 7847
8.1%
3 8133
8.4%

is_male
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.2 KiB
True
49800 
False
47506 
ValueCountFrequency (%)
True 49800
51.2%
False 47506
48.8%
2024-07-30T18:05:40.695859image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

weight_pounds
Real number (ℝ)

Distinct832
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3915582
Minimum1.12
Maximum16.459999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.2 KiB
2024-07-30T18:05:40.983475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile5.5599999
Q16.6900001
median7.3899999
Q38.1099997
95-th percentile9.1899996
Maximum16.459999
Range15.339999
Interquartile range (IQR)1.4199996

Descriptive statistics

Standard deviation1.1006789
Coefficient of variation (CV)0.14891027
Kurtosis0.77834874
Mean7.3915582
Median Absolute Deviation (MAD)0.69999981
Skewness-0.089740433
Sum719242.96
Variance1.211494
MonotonicityNot monotonic
2024-07-30T18:05:41.417259image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 1800
 
1.8%
7.369999886 1708
 
1.8%
7.559999943 1706
 
1.8%
7.25 1684
 
1.7%
7.190000057 1663
 
1.7%
7.440000057 1645
 
1.7%
7.309999943 1626
 
1.7%
7 1608
 
1.7%
7.690000057 1570
 
1.6%
7.630000114 1565
 
1.6%
Other values (822) 80731
83.0%
ValueCountFrequency (%)
1.120000005 1
< 0.1%
1.25 1
< 0.1%
1.289999962 1
< 0.1%
1.5 2
< 0.1%
1.559999943 1
< 0.1%
1.75 1
< 0.1%
1.809999943 1
< 0.1%
1.940000057 1
< 0.1%
2 1
< 0.1%
2.059999943 2
< 0.1%
ValueCountFrequency (%)
16.45999908 1
< 0.1%
15.63000011 1
< 0.1%
14.36999989 1
< 0.1%
13.75 1
< 0.1%
13.31000042 1
< 0.1%
12.85999966 1
< 0.1%
12.43999958 2
< 0.1%
12.39000034 1
< 0.1%
12.18999958 1
< 0.1%
12.13000011 2
< 0.1%

plurality
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size760.3 KiB
1.0
95033 
2.0
 
2245
3.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters291918
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 95033
97.7%
2.0 2245
 
2.3%
3.0 28
 
< 0.1%

Length

2024-07-30T18:05:41.822722image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T18:05:42.104462image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 95033
97.7%
2.0 2245
 
2.3%
3.0 28
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 97306
33.3%
0 97306
33.3%
1 95033
32.6%
2 2245
 
0.8%
3 28
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 97306
33.3%
0 97306
33.3%
1 95033
32.6%
2 2245
 
0.8%
3 28
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 97306
33.3%
0 97306
33.3%
1 95033
32.6%
2 2245
 
0.8%
3 28
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 97306
33.3%
0 97306
33.3%
1 95033
32.6%
2 2245
 
0.8%
3 28
 
< 0.1%

apgar_5min
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9202413
Minimum0
Maximum10
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size760.3 KiB
2024-07-30T18:05:42.376919image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q19
median9
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.60029276
Coefficient of variation (CV)0.067295574
Kurtosis46.005977
Mean8.9202413
Median Absolute Deviation (MAD)0
Skewness-4.8055186
Sum867993
Variance0.3603514
MonotonicityNot monotonic
2024-07-30T18:05:42.697917image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9 81860
84.1%
8 7639
 
7.9%
10 5804
 
6.0%
7 1158
 
1.2%
6 382
 
0.4%
5 198
 
0.2%
4 98
 
0.1%
3 67
 
0.1%
1 40
 
< 0.1%
2 40
 
< 0.1%
ValueCountFrequency (%)
0 20
 
< 0.1%
1 40
 
< 0.1%
2 40
 
< 0.1%
3 67
 
0.1%
4 98
 
0.1%
5 198
 
0.2%
6 382
 
0.4%
7 1158
 
1.2%
8 7639
 
7.9%
9 81860
84.1%
ValueCountFrequency (%)
10 5804
 
6.0%
9 81860
84.1%
8 7639
 
7.9%
7 1158
 
1.2%
6 382
 
0.4%
5 198
 
0.2%
4 98
 
0.1%
3 67
 
0.1%
2 40
 
< 0.1%
1 40
 
< 0.1%

mother_age
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.73424
Minimum13
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.2 KiB
2024-07-30T18:05:43.014291image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile19
Q123
median28
Q332
95-th percentile38
Maximum50
Range37
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9244264
Coefficient of variation (CV)0.21361416
Kurtosis-0.54868687
Mean27.73424
Median Absolute Deviation (MAD)4
Skewness0.21533498
Sum2698708
Variance35.098828
MonotonicityNot monotonic
2024-07-30T18:05:43.420578image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
26 5878
 
6.0%
28 5854
 
6.0%
27 5832
 
6.0%
29 5680
 
5.8%
30 5515
 
5.7%
25 5483
 
5.6%
24 5384
 
5.5%
31 5117
 
5.3%
23 5025
 
5.2%
22 4801
 
4.9%
Other values (28) 42737
43.9%
ValueCountFrequency (%)
13 4
 
< 0.1%
14 57
 
0.1%
15 234
 
0.2%
16 645
 
0.7%
17 1290
 
1.3%
18 2305
2.4%
19 3365
3.5%
20 4002
4.1%
21 4290
4.4%
22 4801
4.9%
ValueCountFrequency (%)
50 6
 
< 0.1%
49 3
 
< 0.1%
48 4
 
< 0.1%
47 23
 
< 0.1%
46 33
 
< 0.1%
45 62
 
0.1%
44 114
 
0.1%
43 251
0.3%
42 402
0.4%
41 612
0.6%

gestation_weeks
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.917543
Minimum35
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.2 KiB
2024-07-30T18:05:43.754157image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile36
Q138
median39
Q340
95-th percentile41
Maximum43
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5921891
Coefficient of variation (CV)0.04091186
Kurtosis0.17076838
Mean38.917543
Median Absolute Deviation (MAD)1
Skewness-0.098785192
Sum3786910.4
Variance2.5350659
MonotonicityNot monotonic
2024-07-30T18:05:44.151884image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
39 26496
27.2%
40 19692
20.2%
38 19406
19.9%
37 9263
 
9.5%
41 9117
 
9.4%
36 4504
 
4.6%
42 3163
 
3.3%
35 2544
 
2.6%
43 1563
 
1.6%
38.95470428 1558
 
1.6%
ValueCountFrequency (%)
35 2544
 
2.6%
36 4504
 
4.6%
37 9263
 
9.5%
38 19406
19.9%
38.95470428 1558
 
1.6%
39 26496
27.2%
40 19692
20.2%
41 9117
 
9.4%
42 3163
 
3.3%
43 1563
 
1.6%
ValueCountFrequency (%)
43 1563
 
1.6%
42 3163
 
3.3%
41 9117
 
9.4%
40 19692
20.2%
39 26496
27.2%
38.95470428 1558
 
1.6%
38 19406
19.9%
37 9263
 
9.5%
36 4504
 
4.6%
35 2544
 
2.6%

cigarette_use
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.2 KiB
False
93792 
True
 
3514
ValueCountFrequency (%)
False 93792
96.4%
True 3514
 
3.6%
2024-07-30T18:05:44.472641image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

alcohol_use
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.2 KiB
False
93716 
True
 
3590
ValueCountFrequency (%)
False 93716
96.3%
True 3590
 
3.7%
2024-07-30T18:05:44.677161image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

weight_gain_pounds
Real number (ℝ)

Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.834937
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.2 KiB
2024-07-30T18:05:44.948767image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q123
median31
Q339.622368
95-th percentile53
Maximum70
Range69
Interquartile range (IQR)16.622368

Descriptive statistics

Standard deviation12.359923
Coefficient of variation (CV)0.38825029
Kurtosis0.051579259
Mean31.834937
Median Absolute Deviation (MAD)8.6223679
Skewness0.17735706
Sum3097730.4
Variance152.76772
MonotonicityNot monotonic
2024-07-30T18:05:45.379803image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.62236786 7599
 
7.8%
30 7211
 
7.4%
40 4813
 
4.9%
25 4702
 
4.8%
20 4637
 
4.8%
35 4303
 
4.4%
32 2530
 
2.6%
28 2353
 
2.4%
50 2277
 
2.3%
33 2224
 
2.3%
Other values (61) 54657
56.2%
ValueCountFrequency (%)
1 165
 
0.2%
2 226
 
0.2%
3 232
 
0.2%
4 272
 
0.3%
5 444
 
0.5%
6 348
 
0.4%
7 469
 
0.5%
8 507
 
0.5%
9 404
 
0.4%
10 1297
1.3%
ValueCountFrequency (%)
70 265
0.3%
69 57
 
0.1%
68 99
 
0.1%
67 82
 
0.1%
66 110
 
0.1%
65 306
0.3%
64 123
0.1%
63 164
0.2%
62 151
0.2%
61 176
0.2%

ever_born
Real number (ℝ)

SKEWED 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0779089
Minimum1
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.2 KiB
2024-07-30T18:05:45.794295image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum231
Range230
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9043474
Coefficient of variation (CV)0.91647302
Kurtosis8582.9336
Mean2.0779089
Median Absolute Deviation (MAD)1
Skewness71.857765
Sum202193
Variance3.6265392
MonotonicityNot monotonic
2024-07-30T18:05:46.185678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 37836
38.9%
2 32574
33.5%
3 16472
16.9%
4 6439
 
6.6%
5 2266
 
2.3%
6 879
 
0.9%
7 407
 
0.4%
8 342
 
0.4%
9 26
 
< 0.1%
10 24
 
< 0.1%
Other values (6) 41
 
< 0.1%
ValueCountFrequency (%)
1 37836
38.9%
2 32574
33.5%
3 16472
16.9%
4 6439
 
6.6%
5 2266
 
2.3%
6 879
 
0.9%
7 407
 
0.4%
8 342
 
0.4%
9 26
 
< 0.1%
10 24
 
< 0.1%
ValueCountFrequency (%)
231 4
 
< 0.1%
15 3
 
< 0.1%
14 5
 
< 0.1%
13 4
 
< 0.1%
12 8
 
< 0.1%
11 17
 
< 0.1%
10 24
 
< 0.1%
9 26
 
< 0.1%
8 342
0.4%
7 407
0.4%

father_age
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.266664
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.2 KiB
2024-07-30T18:05:46.601016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20
Q125
median30
Q335
95-th percentile42
Maximum56
Range43
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7621399
Coefficient of variation (CV)0.22341874
Kurtosis0.075070611
Mean30.266664
Median Absolute Deviation (MAD)5
Skewness0.4634618
Sum2945128
Variance45.726536
MonotonicityNot monotonic
2024-07-30T18:05:47.006007image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
29 5663
 
5.8%
28 5549
 
5.7%
30 5541
 
5.7%
31 5481
 
5.6%
27 5319
 
5.5%
26 5132
 
5.3%
32 5073
 
5.2%
33 4754
 
4.9%
25 4745
 
4.9%
34 4556
 
4.7%
Other values (34) 45493
46.8%
ValueCountFrequency (%)
13 1
 
< 0.1%
14 14
 
< 0.1%
15 53
 
0.1%
16 193
 
0.2%
17 455
 
0.5%
18 1025
 
1.1%
19 1692
1.7%
20 2363
2.4%
21 2962
3.0%
22 3577
3.7%
ValueCountFrequency (%)
56 45
 
< 0.1%
55 50
 
0.1%
54 65
 
0.1%
53 80
 
0.1%
52 123
 
0.1%
51 125
 
0.1%
50 189
0.2%
49 237
0.2%
48 311
0.3%
47 392
0.4%

baby_alive
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.2 KiB
True
89921 
False
 
7385
ValueCountFrequency (%)
True 89921
92.4%
False 7385
 
7.6%
2024-07-30T18:05:47.367292image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Interactions

2024-07-30T18:05:35.127843image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:29.367111image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:32.141001image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:35.444090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:13.475273image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:16.232311image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:18.985138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:21.785412image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:24.629562image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:27.170791image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:32.639350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:35.981603image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:14.010192image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:16.512645image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:19.311666image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:22.100614image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:24.924527image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:36.260987image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:16.782959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:19.669898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:22.406490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:25.211955image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:27.800659image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:28.132293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:31.017460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:33.501883image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:36.840469image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:17.418068image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:20.314518image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:22.985532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:25.785176image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:28.429019image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:23.320370image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:28.761143image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:31.651501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:05:37.361485image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:15.397019image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:18.020975image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:20.937443image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:23.622285image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:26.389837image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:29.075385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:31.899329image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:05:34.435047image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-30T18:05:47.915149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
alcohol_useapgar_5minbaby_alivecigarette_useever_bornfather_agegestation_weeksis_malemonthmother_agepluralityweight_gain_poundsweight_poundsyear
alcohol_use1.0000.0220.0320.9890.0000.0550.0210.0000.0090.0720.0070.0420.0670.056
apgar_5min0.0221.0000.0310.0220.046-0.0000.0380.015-0.003-0.0100.016-0.0020.031-0.083
baby_alive0.0320.0311.0000.0260.0190.0570.0090.0000.0060.0680.0000.0510.0150.202
cigarette_use0.9890.0220.0261.0000.0000.0560.0210.0000.0080.0740.0060.0430.0700.065
ever_born0.0000.0460.0190.0001.0000.288-0.1010.0000.0000.3350.000-0.1340.055-0.013
father_age0.055-0.0000.0570.0560.2881.000-0.0490.0000.0030.7810.037-0.0680.0650.050
gestation_weeks0.0210.0380.0090.021-0.101-0.0491.0000.030-0.000-0.0640.1670.0460.315-0.023
is_male0.0000.0150.0000.0000.0000.0000.0301.0000.0000.0070.0050.0280.1170.000
month0.009-0.0030.0060.0080.0000.003-0.0000.0001.0000.0030.000-0.020-0.009-0.002
mother_age0.072-0.0100.0680.0740.3350.781-0.0640.0070.0031.0000.050-0.0760.0830.059
plurality0.0070.0160.0000.0060.0000.0370.1670.0050.0000.0501.0000.0700.2170.011
weight_gain_pounds0.042-0.0020.0510.043-0.134-0.0680.0460.028-0.020-0.0760.0701.0000.142-0.087
weight_pounds0.0670.0310.0150.0700.0550.0650.3150.117-0.0090.0830.2170.1421.000-0.020
year0.056-0.0830.2020.065-0.0130.050-0.0230.000-0.0020.0590.011-0.087-0.0201.000

Missing values

2024-07-30T18:05:37.764219image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-30T18:05:38.472611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearmonthis_maleweight_poundspluralityapgar_5minmother_agegestation_weekscigarette_usealcohol_useweight_gain_poundsever_bornfather_agebaby_alive
020052False5.372.08.02938.0falsefalse8.010.031True
1200510True6.881.09.03040.0falsefalse25.01.050False
2200510True6.761.09.01938.0falsefalse25.01.024True
3200510False8.691.09.02739.0falsefalse47.01.030True
420059False7.001.09.02040.0falsefalse42.01.030True
520059False8.061.09.03540.0falsefalse12.01.041True
620052True5.181.08.02436.0falsefalse9.01.024True
720055False7.551.09.03240.0falsefalse30.01.042True
820051False5.891.09.03037.0falsefalse31.01.035False
9200511True5.001.09.01837.0falsefalse63.01.024False
yearmonthis_maleweight_poundspluralityapgar_5minmother_agegestation_weekscigarette_usealcohol_useweight_gain_poundsever_bornfather_agebaby_alive
9729619702True8.381.09.02738.954704falsefalse39.6223686.022False
9729719703False7.191.09.02838.954704falsefalse39.6223685.031False
9729819709False6.941.09.02338.954704falsefalse39.6223684.033True
9729919706True7.501.09.04238.954704falsefalse39.6223684.047True
9730019707True6.691.09.02838.954704falsefalse39.6223687.054True
9730119719True8.621.09.02038.954704falsefalse39.6223681.021True
97302197111False6.381.09.01938.954704falsefalse39.6223681.021True
97303197112False7.251.09.01938.954704falsefalse39.6223681.022True
9730419716False6.621.09.02038.954704falsefalse39.6223681.026True
9730519715True6.441.09.01838.954704falsefalse39.6223681.020True

Duplicate rows

Most frequently occurring

yearmonthis_maleweight_poundspluralityapgar_5minmother_agegestation_weekscigarette_usealcohol_useweight_gain_poundsever_bornfather_agebaby_alive# duplicates
020069False7.691.09.02839.0falsefalse30.02.029True2